The influence of input uncertainties on remotely sensed estimates of ocean primary productivity

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Abstract

Temporally and spatially dense estimates of oceanic phytoplankton net primary
productivity (NPP), which are commonly derived by mathematical models from
satellite observations of ocean colour, are a cornerstone of current research
efforts focused on the state and variability of ecosystems, biogeochemical
cycles and climate. Using two exemplary NPP models, it was examined how
uncertainties in model input terms might affect the accuracy of the output.
In the first part of the dissertation, the response of NPP estimates to
perturbing input values of mixed layer depth (MLD) was analyzed. Four series
of NPP fields, two global and two covering the North Atlantic, were computed
in monthly intervals during a period of several years. Each of the series resulted
from identical remote sensing data but different MLD input. Due to the
influence of MLD on the availability of light for photosynthesis, the NPP
estimates were overall inversely related to MLD. However, the degree of this
relationship varied considerably in space and time over most of the world
ocean. During summer, NPP at middle and high latitudes was appreciably
sensitive even to small MLD fluctuations, but had little or no response to large
MLD perturbations in winter. On the other hand, subtropical regions were
characterized by a largely opposite seasonal pattern. Tropical areas showed no
seasonality and, apart from the equatorial Pacific, exhibited little sensitivity of
NPP to MLD uncertainties. The observed variability in the NPP response was
attributed not only to the model’s nonlinearity, but also to the presence of the
photosynthetic saturation/limitation thresholds, as well as to the coincident sea
surface irradiance and, in particular, the diffuse attenuation coefficient for
downward irradiance (Kd). It was shown that Kd could be used as an indicator of
the NPP sensitivity to uncertainties in MLD, the greatest sensitivity being
associated with very large Kd values. Maximum differences between areally
integrated annual NPP estimates, based on different MLD input, were about 20–
30% in the North Atlantic subpolar gyre, about 15–20% in the eastern part of
the North Atlantic subtropical gyre, and less than 10% over the global ocean.
In the second part of the thesis, uncertainties in input terms were propagated
through one of the most widely used NPP models via a Monte Carlo method,
which enabled distinguishing between random and systematic uncertainty
components. The study was based on monthly averaged global remote sensing
observations from 2005. Although, due to computational requirements, the
analysis was restricted to one year only, the results were remarkably stable in
time and space, suggesting that they might also be valid for other years covered
by the satellite observations. The typical distribution of uncertainty around the
model output was lognormal-like. The average random uncertainty in NPP,
expressed as the coefficient of variation, was 108%. The nominal NPP values in
individual grid cells were typically overestimated by 6%, relative to the means
of the associated uncertainty distributions. These positive systematic errors
accumulated to an overestimate of 2.5 Pg C in the annual global NPP of 46.1 Pg
C. The input quantity that contributed most to the systematic uncertainty in NPP was the parameter representing irradiance-dependent vertical changes in
chlorophyll-normalized photosynthetic rates. On the other hand, the largest
contributor to the random uncertainty in NPP was the term describing the
physiological state of phytoplankton. Thus, reductions in the respective
uncertainties in these two input terms could improve the accuracy of the NPP
model the most.
The final part of the thesis presents an analysis of uncertainty associated
with a model of the euphotic depth (Zeu), which was developed for remote
sensing applications and computes Zeu from the near-surface chlorophyll
concentration. The analysis disregarded any uncertainty in the input chlorophyll
values and concentrated only on the intrinsic uncertainty in the Zeu model. The
latter was quantified by comparisons between the Zeu model output and
reference values of Zeu, derived from in situ measured vertical profiles of
downward irradiance. The Zeu model uncertainty, expressed in relative terms,
complied well with a normal distribution. Due to an uneven geographical
coverage of the in situ data set, the uncertainty statistics were weighted with a
global Zeu climatology, obtained from remote sensing. This provided an
estimate of positive bias equal to 9%. The remaining part of Zeu model
uncertainty, which is associated with natural variability, amounted to 22%
(expressed as the zero-centred root mean square difference).

Paper II: Remote Sensing of Environment 115(8), Svetlana Milutinović and Laurent Bertino, Assessment and Propagation of Uncertainties in Input Terms through an Ocean-Colour-Based Model of Primary Productivity. Accepted version. Copyright 2011 Elsevier. The published version is available at: http://dx.doi.org/10.1016/j.rse.2011.03.013